
#Import Library 
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import warnings
warnings.filterwarnings('ignore')


df = pd.read_csv("D:\\Projects\\PRJ Car Price Prediction\\audi.csv")
df


os.getcwd()

'C:\\Users\\Lenovo'

df


#Pandas Profiling Report 
import pandas_profiling as pf
display(pf.ProfileReport(df))

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#Number of records
print (len(df))

10668


#Number of records- Shape
display (df.shape)

(10668, 9)


#Checking the data types
display (df.dtypes )

model            object
year              int64
price             int64
transmission     object
mileage           int64
fuelType         object
tax               int64
mpg             float64
engineSize      float64
dtype: object


#Checking null values
display (df.isna().sum())

model           0
year            0
price           0
transmission    0
mileage         0
fuelType        0
tax             0
mpg             0
engineSize      0
dtype: int64


#Data set details – Info 
print (df.info())

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10668 entries, 0 to 10667
Data columns (total 9 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   model         10668 non-null  object 
 1   year          10668 non-null  int64  
 2   price         10668 non-null  int64  
 3   transmission  10668 non-null  object 
 4   mileage       10668 non-null  int64  
 5   fuelType      10668 non-null  object 
 6   tax           10668 non-null  int64  
 7   mpg           10668 non-null  float64
 8   engineSize    10668 non-null  float64
dtypes: float64(2), int64(4), object(3)
memory usage: 750.2+ KB
None


#Data set details – Describe 
display (df.describe ())


#Create X 
X = df.iloc[:,[0,1,3,4,5,6,7,8]].values
display (X.shape)
display (X)

(10668, 8)

array([[' A1', 2017, 'Manual', ..., 150, 55.4, 1.4],
       [' A6', 2016, 'Automatic', ..., 20, 64.2, 2.0],
       [' A1', 2016, 'Manual', ..., 30, 55.4, 1.4],
       ...,
       [' A3', 2020, 'Manual', ..., 150, 49.6, 1.0],
       [' Q3', 2017, 'Automatic', ..., 150, 47.9, 1.4],
       [' Q3', 2016, 'Manual', ..., 150, 47.9, 1.4]], dtype=object)


#Create Y
Y = df.iloc[:,[2]].values
display (Y.shape)
display (Y)

(10668, 1)

array([[12500],
       [16500],
       [11000],
       ...,
       [17199],
       [19499],
       [15999]], dtype=int64)


#Display Top 5 - X variable 
display(pd.DataFrame(X).head(5))


#Display Top 5 - Y variable 
display(pd.DataFrame(Y).head(5))


#Label Encoding Column – Model and Fuel Type
from sklearn.preprocessing import LabelEncoder
le1 = LabelEncoder()
X[:,0] = le1.fit_transform(X[:,0])
le2 = LabelEncoder()
X[:,-4] = le2.fit_transform(X[:,-4])
display (X)

array([[0, 2017, 'Manual', ..., 150, 55.4, 1.4],
       [5, 2016, 'Automatic', ..., 20, 64.2, 2.0],
       [0, 2016, 'Manual', ..., 30, 55.4, 1.4],
       ...,
       [2, 2020, 'Manual', ..., 150, 49.6, 1.0],
       [9, 2017, 'Automatic', ..., 150, 47.9, 1.4],
       [9, 2016, 'Manual', ..., 150, 47.9, 1.4]], dtype=object)


#One hot Encoding to column – transmission
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
ct = ColumnTransformer(transformers = [('encoder',OneHotEncoder(),[2])],remainder='passthrough')
X = ct.fit_transform(X)
display (X.shape)
display (pd.DataFrame(X))

(10668, 10)


#Display – X 
display (pd.DataFrame(X))


#Features Scaling – Standardization 

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X = sc.fit_transform(X)
display (pd.DataFrame(X))


#Train Test Split 
from sklearn.model_selection import train_test_split
(X_train,X_test,Y_train,Y_test) = train_test_split(X,Y,test_size=0.2,random_state=0)
print (X.shape, Y.shape)
print (X_train.shape, Y_train.shape)
print (X_test.shape, Y_test.shape)

(10668, 10) (10668, 1)
(8534, 10) (8534, 1)
(2134, 10) (2134, 1)


#Create Random Forest Regressor 

from sklearn.ensemble import RandomForestRegressor
regression = RandomForestRegressor(random_state=0)
regression.fit(X_train,Y_train)
display (regression)

RandomForestRegressor(random_state=0)

RandomForestRegressor(random_state=0)


#Prediction with Test Data 
y_pred = regression.predict(X_test)
display (y_pred)

array([14337.15, 23450.35, 27330.07, ..., 46275.18, 31359.  ,  9929.62])


#Display Predicted and actual Values resp. 

print(np.concatenate((y_pred.reshape(len(y_pred),1),Y_test.reshape(len(Y_test),1)),1))

[[14337.15 14998.  ]
 [23450.35 21950.  ]
 [27330.07 28990.  ]
 ...
 [46275.18 45995.  ]
 [31359.   30500.  ]
 [ 9929.62  8400.  ]]


#Display – Accuracy and Mean Absolute Error 

from sklearn.metrics import r2_score,mean_absolute_error
print  ('R2 Score ', r2_score(Y_test, y_pred))
print  ('Mean Absolute Error', mean_absolute_error(Y_test,y_pred))

R2 Score  0.9536134841307546
Mean Absolute Error 1538.730980670462


#Create a Linear Regression Model 

from sklearn.linear_model import LinearRegression
reg = LinearRegression()
reg.fit(X_train,Y_train)
print(reg)

LinearRegression()


#Prediction with Test Data 
y_pred = reg.predict(X_test)
display (y_pred)

array([[13095.55234668],
       [29380.55234668],
       [31837.55234668],
       ...,
       [42652.55234668],
       [31554.55234668],
       [ 7329.55234668]])


#Display predicted and actual Values 

print(np.concatenate((y_pred.reshape(len(y_pred),1),Y_test.reshape(len(Y_test),1)),1))

[[13095.55234668 14998.        ]
 [29380.55234668 21950.        ]
 [31837.55234668 28990.        ]
 ...
 [42652.55234668 45995.        ]
 [31554.55234668 30500.        ]
 [ 7329.55234668  8400.        ]]


#Display – Accuracy and Mean Absolute Error 

from sklearn.metrics import r2_score,mean_absolute_error
print  ('R2 Score ', r2_score(Y_test, y_pred))
print  ('Mean Absolute Error', mean_absolute_error(Y_test,y_pred))

R2 Score  0.7915568279145352
Mean Absolute Error 3379.644179345178


#Prediction for complete data set 
y_pred = reg.predict(X)
display (y_pred)

array([[14660.55234668],
       [20566.55234668],
       [13842.55234668],
       ...,
       [19436.55234668],
       [20924.55234668],
       [16720.55234668]])


#Display the Actual and predicted data  

result = pd.concat([df,pd.DataFrame(y_pred)],axis=1)
display( result)


#Create Model Extra Tree Regressor 
from sklearn.ensemble import  ExtraTreesRegressor
ET_Model=ExtraTreesRegressor(n_estimators = 120)
ET_Model.fit(X_train,Y_train)
y_predict=ET_Model.predict(X_test)
from sklearn.metrics import r2_score,mean_absolute_error
print  ('R2 Score ', r2_score(Y_test, y_predict))
print  ('Mean Absolute Error', mean_absolute_error(Y_test,y_predict))

R2 Score  0.9570765070738761
Mean Absolute Error 1533.2059619389775


#Display the Result 
y_pred = reg.predict(X)
display (y_pred)
result = pd.concat([df,pd.DataFrame(y_pred)],axis=1)
display( result)

array([[14660.55234668],
       [20566.55234668],
       [13842.55234668],
       ...,
       [19436.55234668],
       [20924.55234668],
       [16720.55234668]])


#RandomizedSearchCV
# Hyperparameter Tuning and RandomizedSearchCV - Model used – RandomForestRegressor

from sklearn.model_selection import RandomizedSearchCV
n_estimators = [int(x) for x in np.linspace(start = 80, stop = 1500, num = 10)]
max_features = ['auto', 'sqrt']
max_depth = [int(x) for x in np.linspace(6, 45, num = 5)]
min_samples_split = [2, 5, 10, 15, 100]
min_samples_leaf = [1, 2, 5, 10]

# create random grid

rand_grid={'n_estimators': n_estimators,
               'max_features': max_features,
               'max_depth': max_depth,
               'min_samples_split': min_samples_split,
               'min_samples_leaf': min_samples_leaf}

rf=RandomForestRegressor()

rCV=RandomizedSearchCV(estimator=rf,param_distributions=rand_grid,scoring='neg_mean_squared_error',n_iter=3,cv=3,random_state=42, n_jobs = 1)


#Fit Model

display (rCV.fit(X_train,Y_train))

RandomizedSearchCV(cv=3, estimator=RandomForestRegressor(), n_iter=3, n_jobs=1,
                   param_distributions={'max_depth': [6, 15, 25, 35, 45],
                                        'max_features': ['auto', 'sqrt'],
                                        'min_samples_leaf': [1, 2, 5, 10],
                                        'min_samples_split': [2, 5, 10, 15,
                                                              100],
                                        'n_estimators': [80, 237, 395, 553, 711,
                                                         868, 1026, 1184, 1342,
                                                         1500]},
                   random_state=42, scoring='neg_mean_squared_error')

RandomizedSearchCV(cv=3, estimator=RandomForestRegressor(), n_iter=3, n_jobs=1,
                   param_distributions={'max_depth': [6, 15, 25, 35, 45],
                                        'max_features': ['auto', 'sqrt'],
                                        'min_samples_leaf': [1, 2, 5, 10],
                                        'min_samples_split': [2, 5, 10, 15,
                                                              100],
                                        'n_estimators': [80, 237, 395, 553, 711,
                                                         868, 1026, 1184, 1342,
                                                         1500]},
                   random_state=42, scoring='neg_mean_squared_error')

RandomForestRegressor()

RandomForestRegressor()


#Prediction 
rf_pred=rCV.predict(X_test)


#Mean_absolute_error and mean_squared_error

from sklearn.metrics import mean_absolute_error,mean_squared_error
print('MAE',mean_absolute_error(Y_test,rf_pred))
print('MSE',mean_squared_error(Y_test,rf_pred))

MAE 1504.3805344295797
MSE 5677323.888543681


#Display Accuracy
display (r2_score(Y_test,rf_pred))

0.9586851357630621


#Model CatBoostRegressor
from catboost import CatBoostRegressor
cat=CatBoostRegressor()
print (cat.fit(X_train,Y_train))

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<catboost.core.CatBoostRegressor object at 0x0000020428F4F890>


#Cat Boost Prediction 
cat_pred=cat.predict(X_test)
display (cat_pred)

array([13386.63817795, 24056.86842882, 28082.99514751, ...,
       45959.31234642, 31714.44751009,  9481.45994163])


#Cat Boost Accuracy
display (r2_score(Y_test,cat_pred))

0.9641612028134969


#Create Pickle File 
#Use pickle to save our model so that we can use it later
import pickle 
# Saving model to disk
pickle.dump(cat, open('model.pkl','wb'))


#Load Pickle File and  do Prediction  
model=pickle.load(open('model.pkl','rb'))
print (model.predict (X_train))

[14256.69390956 29475.32741129 11911.12888914 ... 21406.16876331
 17189.65426694 44839.56117235]


#EXPLORATORY DATA ANALYSIS
import pandas_profiling as pf
display(pf.ProfileReport(df))

Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]

Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]

Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]

	year	price	mileage	tax	mpg	engineSize
count	10668.000000	10668.000000	10668.000000	10668.000000	10668.000000	10668.000000
mean	2017.100675	22896.685039	24827.244001	126.011436	50.770022	1.930709
std	2.167494	11714.841888	23505.257205	67.170294	12.949782	0.602957
min	1997.000000	1490.000000	1.000000	0.000000	18.900000	0.000000
25%	2016.000000	15130.750000	5968.750000	125.000000	40.900000	1.500000
50%	2017.000000	20200.000000	19000.000000	145.000000	49.600000	2.000000
75%	2019.000000	27990.000000	36464.500000	145.000000	58.900000	2.000000
max	2020.000000	145000.000000	323000.000000	580.000000	188.300000	6.300000

	0	1	2	3	4	5	6	7	8	9
0	-0.583268	1.200728	-0.712333	-1.123544	-0.046450	-0.386836	1.050783	0.357147	0.357550	-0.880218
1	1.714479	-0.832828	-0.712333	-0.160831	-0.507834	0.483989	-0.954181	-1.578323	1.037130	0.114925
2	-0.583268	1.200728	-0.712333	-1.123544	-0.507834	0.217781	1.050783	-1.429440	0.357550	-0.880218
3	1.714479	-0.832828	-0.712333	-0.545916	-0.046450	0.047853	-0.954181	0.282706	1.276528	0.114925
4	-0.583268	1.200728	-0.712333	-0.738459	0.876318	-0.971285	1.050783	0.282706	-0.090355	-1.543647
...	...	...	...	...	...	...	...	...	...	...
10663	-0.583268	1.200728	-0.712333	-0.738459	1.337702	-0.885343	1.050783	0.282706	-0.090355	-1.543647
10664	-0.583268	1.200728	-0.712333	-0.738459	1.337702	-0.972136	1.050783	0.357147	-0.090355	-1.543647
10665	-0.583268	1.200728	-0.712333	-0.738459	1.337702	-1.030381	1.050783	0.357147	-0.090355	-1.543647
10666	1.714479	-0.832828	-0.712333	0.609339	-0.046450	-0.688442	1.050783	0.357147	-0.221637	-0.880218
10667	-0.583268	1.200728	-0.712333	0.609339	-0.507834	-0.551913	1.050783	0.357147	-0.221637	-0.880218

	model	year	price	transmission	mileage	fuelType	tax	mpg	engineSize
0	A1	2017	12500	Manual	15735	Petrol	150	55.4	1.4
1	A6	2016	16500	Automatic	36203	Diesel	20	64.2	2.0
2	A1	2016	11000	Manual	29946	Petrol	30	55.4	1.4
3	A4	2017	16800	Automatic	25952	Diesel	145	67.3	2.0
4	A3	2019	17300	Manual	1998	Petrol	145	49.6	1.0
...	...	...	...	...	...	...	...	...	...
10663	A3	2020	16999	Manual	4018	Petrol	145	49.6	1.0
10664	A3	2020	16999	Manual	1978	Petrol	150	49.6	1.0
10665	A3	2020	17199	Manual	609	Petrol	150	49.6	1.0
10666	Q3	2017	19499	Automatic	8646	Petrol	150	47.9	1.4
10667	Q3	2016	15999	Manual	11855	Petrol	150	47.9	1.4